Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Greedy Sampling for Approximate Clustering in the Presence of Outliers
Authors: Aditya Bhaskara, Sharvaree Vadgama, Hong Xu
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,258 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,077 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the empirical performance of our algorithm on multiple real and synthetic datasets, and compare it to existing heuristics. |
| Researcher Affiliation | Academia | Aditya Bhaskara University of Utah EMAIL Sharvaree Vadgama University of Utah EMAIL Hong Xu University of Utah EMAIL |
| Pseudocode | Yes | Algorithm 1 k-center with outliers |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-sourcing the code for the methodology described. |
| Open Datasets | Yes | All real datasets we use are available from the UCI repository [15]. |
| Dataset Splits | No | The paper mentions datasets used but does not provide specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any particular hardware (GPU, CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In order to simulate corruptions, we randomly choose 2.5% of the points in the datasets and corrupt all the coordinates by adding independent noise in a pre-deο¬ned range. |